The present invention is related to robotic manipulation of an item to substantially improve the dexterity of robotic manipulation in light of uncertainty. Uncertainty is present in the hand-tool interface: currently, it is still close to impossible for a robotic hand to grasp objects at predetermined contact points and forces. Moreover, uncertainty is present in the tool-environment interface (e.g., between a pen tip and paper) as friction is still hard to model and to predict. Prior methods that analytically model a grasp failed under such uncertainty.
Previous efforts in the area of robotic manipulation under uncertainty focused on using low-gain compliant control. Such control avoids hard collisions, but cannot provide precision control if required (e.g., for writing). To improve precision, some efforts used learning methods to compute control torques without increasing the control gains for trajectory tracking. Through random exploration (motor babbling), the robot learns the kinematic and dynamic relationship between joint angles or torques and hand position. These efforts were limited to learning the kinematics and dynamics of the robot arm itself and thus could not cope with an uncertain interface between robot gripper (e.g., hand) and manipulated object.
Recent prior art has dealt with the uncertain interface between the gripper and object. For example, researchers Kemp and Edsinger found a method to obtain the position of a tool tip without knowing the contact point between tool and gripper. They described their process at “Robot manipulation of human tools: Autonomous detection and control of task relevant features,” 5th IEEE International Conference on Development and Learning, 2006. As described by Kemp and Edsinger, the robot determines the tip position by waving the gripper and computing the image location of highest speed. While operable for determining the tip position, the method is limited to certain tool shapes and requires visual feedback via a visual sensor (e.g., video camera).
As opposed to visual sensor, tactile sensors sense a contact sensation. However, existing tactile sensors are still very noisy and have not been previously used to analyze the uncertain interface between the gripper and tool. So far, the utility of tactile sense in robots is largely reduced to on/off switches. In contrast, humans greatly enhance their manual dexterity through tactile sense. Blind people demonstrate that great dexterity is possible using only tactile feedback. In addition, fine motor skills in healthy humans are hampered if tactile sense is removed (e.g., lighting a match with anaesthetized fingers is almost impossible). Thus, if tactile feedback can be efficiently exploited, robotic manipulation will become more feasible in human-like settings.
In summary, over the last several decades, many research groups around the world have worked on robotic manipulation, but the uncertainty of a grasp has prohibited dexterous tool use. Tactile sense has not been used efficiently for manipulation. The robotic field focused either on predicting sensory input analytically or triggering purely reactive behavior given sensory input. The first is limited by the noise of the sensory input, and the second prohibits gradual change of force application.
Thus, a continuing need exists to extend robotic control into the tactile domain by allowing a more gradual change of force application that smoothly adapts to changes in the environment (e.g., surface slope for writing).